Postdoctoral Research Associate - Computational Materials Scientist for Structural Lightweight Alloy

Requisition Id 14081 

­­Overview:  

We are seeking a Postdoctoral Research Associate to join our multidisciplinary team who will focus on leveraging Integrated Computational Materials Engineering (ICME) approaches combined with machine learning (ML) and artificial intelligence (AI) to accelerate the discovery and design of advanced structural alloys for diverse industrial applications. This position resides in the Microstructural Evolution Modeling Group in the Materials Sciences and Technology Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL).  

 

This role involves a close collaboration with experimentalists who fabricate and characterize lightweight alloys, creating a synergistic loop between computational predictions and experimental validation. Utilizing a computational thermodynamic framework, specifically the CALPHAD method, and integrating modern data analytics, you will interpret experimental data, uncover fundamental insights, and guide the development of next-generation alloys with enhanced performance. This position provides an excellent opportunity to contribute to cutting-edge materials research and gain experience in interdisciplinary collaboration.

 

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 7,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.

 

Major Duties/Responsibilities: 

• Conduct alloy research using computational thermodynamics, focusing on high-throughput computations integrated with data analytics for alloy discovery and design.
• Work with members of dynamic research groups to develop theoretical approaches to support the development of next generation high-temperature materials for harsh environments.
• Responsible for presenting and reporting research and publishing scientific results in peer-reviewed journals in a timely manner.
• Maintain strong dedication to the implementation and perpetuation of values and ethics of the institution.

• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.

 

Basic Qualifications:

• A PhD in Materials Science & Engineering, Physics, Chemistry, or a related field completed within the last 5 years.
• A minimum of two years of experience utilizing computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage.
• Proficiency in materials data analytics, including correlation analysis and machine learning techniques.

 

Preferred Qualifications:

•  Comprehensive understanding of ICME.
• A strong record of publications in peer-reviewed journals to demonstrate research productivity and creativity.
• Proven expertise in kinetic and microstructural modeling, with demonstrated experience in developing and applying simulation tools to predict microstructural evolution and phase transformations.
• Strong background in atomistic simulations, such as density functional theory (DFT) and/or molecular dynamics (MD), with a preference for experience leveraging high-performance computing (HPC) platforms to perform large-scale simulations.

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